We also wanted to investigate what happens when we run a similar analysis, but we split groups based on DFR accuracy.

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(reshape2)
library(psych)
## 
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha
library(patchwork)
library(rockchalk)
## 
## Attaching package: 'rockchalk'
## The following object is masked from 'package:dplyr':
## 
##     summarize
load('data/load_effects_DFR.RData')
load('data/behav.RData')
load('data/structural_measures.RData')
load('data/connectivity_data.RData')

source("split_into_groups.R")
source("prep_split_for_bar_plots.R")
source("plot_bars.R")
acc <- p200_data[p200_data$PTID %in% p200_indiv_ROI_DFR_delay$PTID,]
acc <- merge(acc,construct_vars_omnibus)

data_for_plot <- merge(p200_indiv_ROI_DFR_delay,acc)

p1 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_ROIs))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.85,y=2.25,label="r = 0.25**")

p2 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_L_dlPFC))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1,label="r = 0.26***")

p3 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_L_IPS))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.85,y=.9,label="r = 0.24**")



p4 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_L_preSMA))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1,label="r = 0.28***")


(p1+p2)/(p3+p4)

#sort by omnibus span
p200_data_sorted <- acc[order(acc$XDFR_MRI_ACC_L3),]

low_DFR <- p200_data_sorted[1:56,]
med_DFR <- p200_data_sorted[58:113,]
high_DFR <-p200_data_sorted[115:170,]
low_DFR$level <- "low"
med_DFR$level <- "med"
high_DFR$level <- "high"

things_to_hist <- rbind(low_DFR,med_DFR,high_DFR)

DFR_groups <- list(high=data.frame(high_DFR),med=data.frame(med_DFR), low=data.frame(low_DFR), all=data.frame(things_to_hist))

save(list=c("DFR_groups", "p200_data_sorted"),file="data/DFR_split_groups_info.RData")
low_med_split_DFR <- p200_data_sorted[1:85,]
high_med_split_DFR <- p200_data_sorted[86:170,]

low_med_split_DFR$level <- "low_median"
high_med_split_DFR$level <- "high_median"

Compare how group IDs change

load("~/Documents/Code/RDoC_for_GitHub/data/split_WM_groups_fMRI.RData")
DFR_med_split <- rbind(low_med_split_DFR,high_med_split_DFR)

DFR_split_data <- dplyr::select(DFR_groups[["all"]],"PTID","XDFR_MRI_ACC_L3","level") 
WM_split_data <- dplyr::select(split_constructs[["all"]],"PTID","omnibus_span_no_DFR_MRI","level")
DFR_med_split_data <- dplyr::select(DFR_med_split,"PTID","level")
colnames(DFR_split_data)[3] <- "DFR_level"
colnames(WM_split_data)[3] <- "WM_level"
colnames(DFR_med_split_data)[2] <- "DFR_med_level"

comparison <- merge(DFR_split_data,WM_split_data,by="PTID")
comparison <- merge(comparison, DFR_med_split_data,by="PTID")

comparison <- dplyr::select(comparison,"PTID", "omnibus_span_no_DFR_MRI","XDFR_MRI_ACC_L3", "WM_level","DFR_level","DFR_med_level")

comparison_counts <- data.frame(matrix(nrow=3,ncol=3))
colnames(comparison_counts) <- c("low_WM","med_WM","high_WM")
rownames(comparison_counts) <- c("low_DFR","med_DFR","high_DFR")

for (row in seq.int(1,3)){
  for (col in seq.int(1,3)){
    comparison_counts[row,col] <- sum(substr(comparison$WM_level,1,3)==substr(colnames(comparison_counts)[row],1,3) & substr(comparison$DFR_level,1,3)==substr(colnames(comparison_counts)[col],1,3))
    
  }
}

comparison_counts_melt <- data.frame(matrix(ncol=3,nrow=9))
colnames(comparison_counts_melt) <- c("WM","DFR","count")
row_count <- 1
for (row in seq.int(1,3)){
  for (col in seq.int(1,3)){ 
    comparison_counts_melt$WM[row_count] <- colnames(comparison_counts)[row]
    comparison_counts_melt$DFR[row_count] <- rownames(comparison_counts)[col]
    comparison_counts_melt$count[row_count] <- comparison_counts[row,col]
    row_count = row_count+1
  }
}

comparison_counts_melt$WM <- factor(comparison_counts_melt$WM,levels = c("low_WM","med_WM","high_WM"))
comparison_counts_melt$DFR <- factor(comparison_counts_melt$DFR,levels=c("low_DFR","med_DFR","high_DFR"))

Subjects split on DFR high load accuracy. 56 subjects per group. Approximately 50% of subjects stay in their initial group.

ggplot(data=comparison_counts_melt)+
  geom_tile(aes(x=WM,y=DFR, fill=count))+
  geom_text(aes(x=WM,y=DFR,label=count),size=8,color="white")+ 
  theme(legend.position = "none") 

comparison_counts_median <- data.frame(matrix(nrow=2,ncol=3))
colnames(comparison_counts_median) <- c("low_WM","med_WM","high_WM")
rownames(comparison_counts_median) <- c("low_DFR_median","high_DFR_median")

comparison_counts_median[1,1] <- sum(comparison$WM_level == "low" & comparison$DFR_med_level == "low_median")
comparison_counts_median[2,1] <- sum(comparison$WM_level == "low" & comparison$DFR_med_level == "high_median")

comparison_counts_median[1,2] <- sum(comparison$WM_level == "med" & comparison$DFR_med_level == "low_median")
comparison_counts_median[2,2] <- sum(comparison$WM_level == "med" & comparison$DFR_med_level == "high_median")

comparison_counts_median[1,3] <- sum(comparison$WM_level == "high" & comparison$DFR_med_level == "low_median")
comparison_counts_median[2,3] <- sum(comparison$WM_level == "high" & comparison$DFR_med_level == "high_median")


comparison_counts_median_melt <- data.frame(matrix(ncol=3,nrow=6))
colnames(comparison_counts_median_melt) <- c("WM","DFR_median","count")
row_count <- 1
for (row in seq.int(1,2)){
  for (col in seq.int(1,3)){ 
    comparison_counts_median_melt$WM[row_count] <- colnames(comparison_counts_median)[col]
    comparison_counts_median_melt$DFR_median[row_count] <- rownames(comparison_counts_median)[row]
    comparison_counts_median_melt$count[row_count] <- comparison_counts_median[row,col]
    row_count = row_count+1
  }
}

comparison_counts_median_melt$WM <- factor(comparison_counts_median_melt$WM,levels = c("low_WM","med_WM","high_WM"))
comparison_counts_median_melt$DFR <- factor(comparison_counts_median_melt$DFR_median,levels=c("low_DFR_median","high_DFR_median"))

Later, we’re going to make our lives easier by just looking at DFR split on median (high scorers and low scorers), so let’s look at this as well.

We tend to see that if you have at least medium capacity, you tend to do well on the task.

ggplot(data=comparison_counts_median_melt)+
  geom_tile(aes(x=WM,y=DFR_median, fill=count))+
  geom_text(aes(x=WM,y=DFR_median,label=count),size=8,color="white")

split_constructs <- split_into_groups(constructs_fMRI[1:7],DFR_groups)
split_clinical <- split_into_groups(p200_clinical_zscores, DFR_groups)
split_DFR_delay <- split_into_groups(p200_indiv_ROI_DFR_delay, DFR_groups)
split_DFR_cue <- split_into_groups(p200_indiv_ROI_DFR_cue, DFR_groups)
split_DFR_probe <- split_into_groups(p200_indiv_ROI_DFR_probe, DFR_groups)
split_DFR_FFA <- split_into_groups(p200_FFA,DFR_groups)
split_DFR_HPC_Ant <- split_into_groups(p200_HPC_Ant, DFR_groups)
split_DFR_HPC_Med <- split_into_groups(p200_HPC_Med, DFR_groups)
split_DFR_HPC_Post <- split_into_groups(p200_HPC_Post, DFR_groups)
split_fullMask_delay <- split_into_groups(p200_DFR_full_mask, DFR_groups)
split_cue_ROIs <- split_into_groups(p200_indiv_ROI_delayDFR_cuePeriod, DFR_groups)
split_demographics <- split_into_groups(p200_demographics,DFR_groups)
split_cortical_thickness_DFR <- split_into_groups(p200_DFR_fullMask_cortical_thickness,DFR_groups)
split_RS <- split_into_groups(p200_all_RS,DFR_groups)
split_beta_conn_cue <- split_into_groups(p200_beta_conn_cue,DFR_groups)
split_beta_conn_delay <- split_into_groups(p200_beta_conn_delay,DFR_groups)
split_BCT <- split_into_groups(p200_BCT_forCorr,DFR_groups)
split_indiv_partic_coeff <- split_into_groups(p200_indiv_network_ParticCoeff,DFR_groups)
save(list=c("split_constructs","split_clinical","split_DFR_delay", "split_DFR_cue", "split_DFR_probe", "split_DFR_FFA", "split_DFR_HPC_Ant", "split_DFR_HPC_Med", "split_DFR_HPC_Post", "split_fullMask_delay", "split_cue_ROIs", "split_demographics","split_cortical_thickness_DFR","split_RS","split_beta_conn_cue","split_beta_conn_delay","split_BCT", "split_indiv_partic_coeff"), file="data/split_DFR_groups_fMRI.RData")
split_means_demo <- data.frame(matrix(nrow=length(split_demographics)-1,ncol=8))
colnames(split_means_demo) <- c("Trio","Prisma","CS","NCS","female","male","age","age_se")
rownames(split_means_demo) <- names(split_demographics)[1:length(names(split_demographics))-1]

for (level in seq.int(1,length(split_demographics)-1)){
  split_means_demo$Trio[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==1])
  split_means_demo$Prisma[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==2])
  split_means_demo$CS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==1])
  split_means_demo$NCS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==2])
  split_means_demo$female[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==2])
  split_means_demo$male[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==1])
  split_means_demo$age[level] <- mean(split_demographics[[level]]$AGE,na.rm=TRUE)
  split_means_demo$age_se[level] <- sd(split_demographics[[level]]$AGE,na.rm=TRUE)/sqrt(length(split_demographics[[level]]$AGE[!is.na(split_demographics[[level]]$AGE)]))
  
}

split_means_demo$level <- as.factor(c("high", "med","low"))
means_melt_demo <- melt(split_means_demo,id.vars="level")

Demographics

Most notable thing here is that there is a larger proportion of CS than NCS in the low performing group. Also seeing that there are more females in the mid performing group.

age_plot <- ggplot(data=split_means_demo,aes(x=level,y=age))+
  geom_bar(stat="identity",width = .5, color = "#667Ea4", fill = "#667Ea4")+
  geom_errorbar(aes(ymin=age-age_se,ymax=age+age_se),width=.2)+
  ggtitle("Age") +
  ylab("Mean +/- SE") +
  scale_x_discrete(limits = c("low","med","high")) +
  theme(aspect.ratio = 1)

scanner_data <- demo_plot_data.m[demo_plot_data.m$variable=="scanner_count",c(1,2,5,6)]
scanner_data$value <- scanner_data$value/56*100
scanner_plot <- ggplot(scanner_data,aes(x=level,y=value,fill=scanner))+
  geom_bar(stat="identity") +
  ylab("Percent (%)") +
  theme(aspect.ratio=1) +
  scale_x_discrete(limits = c("low","med","high")) +
  ggtitle("Scanner")

gender_data <- demo_plot_data.m[demo_plot_data.m$variable=="gender_count",c(1,3,5,6)]
gender_data$value <- gender_data$value/56*100
gender_plot <- ggplot(gender_data,aes(x=level,y=value, fill=gender))+
  geom_bar(stat="identity") +
  ylab("Percent (%)") +
  theme(aspect.ratio=1) +
  scale_x_discrete(limits = c("low","med","high")) +
  ggtitle("Gender")


care_data <- demo_plot_data.m[demo_plot_data.m$variable=="care_count",c(1,4:6)]
care_data$value <- care_data$value/56*100
care_plot <- ggplot(care_data,aes(x=level,y=value, fill=care))+
  geom_bar(stat="identity") +
  ylab("Percent (%)") +
  theme(aspect.ratio=1) +
  scale_x_discrete(limits = c("low","med","high")) +
  ggtitle("CS vs NCS")

(age_plot + gender_plot)/(care_plot + scanner_plot)+
  plot_annotation(title="Demographics split by DFR performance")

melt_constructs <- prep_split_for_bar_plots(DFR_groups)
melt_clinical <- prep_split_for_bar_plots(split_clinical)
melt_DFR_delay <- prep_split_for_bar_plots(split_DFR_delay)
melt_DFR_cue <- prep_split_for_bar_plots(split_DFR_cue)
melt_DFR_probe <- prep_split_for_bar_plots(split_DFR_probe)
melt_DFR_FFA <- prep_split_for_bar_plots(split_DFR_FFA)
melt_DFR_HPC_Ant <- prep_split_for_bar_plots(split_DFR_HPC_Ant)
melt_DFR_HPC_Med <- prep_split_for_bar_plots(split_DFR_HPC_Med)
melt_DFR_HPC_Post <- prep_split_for_bar_plots(split_DFR_HPC_Post)
melt_fullMask_delay <- prep_split_for_bar_plots(split_fullMask_delay)
melt_cue_ROIs <- prep_split_for_bar_plots(split_cue_ROIs)
melt_cortical_thickness_DFR <- prep_split_for_bar_plots(split_cortical_thickness_DFR)
melt_RS <- prep_split_for_bar_plots(split_RS)
melt_beta_conn_cue <- prep_split_for_bar_plots(split_beta_conn_cue)
melt_beta_conn_delay <- prep_split_for_bar_plots(split_beta_conn_delay)
melt_BCT <- prep_split_for_bar_plots(split_BCT)
melt_indiv_partic_coeff <- prep_split_for_bar_plots(split_indiv_partic_coeff)
constructs_plots <- plot_bars(melt_constructs)
clinical_plots <- plot_bars(melt_clinical)
DFR_delay_plots <- plot_bars(melt_DFR_delay)
DFR_cue_plots <- plot_bars(melt_DFR_cue)
DFR_probe_plots <- plot_bars(melt_DFR_probe)
DFR_FFA_plots <- plot_bars(melt_DFR_FFA)
DFR_HPC_Ant_plots <- plot_bars(melt_DFR_HPC_Ant)
DFR_HPC_Med_plots <- plot_bars(melt_DFR_HPC_Med)
DFR_HPC_Post_plots <- plot_bars(melt_DFR_HPC_Post)
fullMask_delay_plots <- plot_bars(melt_fullMask_delay)
cue_ROIs_plots <- plot_bars(melt_cue_ROIs)
cortical_thickness_plots <- plot_bars(melt_cortical_thickness_DFR)
RS_plots <- plot_bars(melt_RS)
beta_conn_cue_plots <- plot_bars(melt_beta_conn_cue)
beta_conn_delay_plots <- plot_bars(melt_beta_conn_delay)
BCT_plots <- plot_bars(melt_BCT)
indiv_partic_coeff_plots <- plot_bars(melt_indiv_partic_coeff)

Constructs

A nice sanity check here as well - if a subject has higher capacity and higher intelligence, they tend to have higher performance. The main statistically significant differences here are in omnibus span, where high > low.

constructs_plots[["omnibus_span_no_DFR_MRI"]]$labels$title = "Omnibus Span"

(constructs_plots[["omnibus_span_no_DFR_MRI"]]+constructs_plots[["intelligence"]]+constructs_plots[["LTM"]]) +
  plot_annotation(title="Constructs split on DFR performance")

print("Omnibus Span")
## [1] "Omnibus Span"
span.aov <- aov(omnibus_span_no_DFR_MRI ~ level, data=split_constructs[["all"]])
summary(span.aov)
##              Df Sum Sq Mean Sq F value  Pr(>F)   
## level         2   2.84  1.4193   5.096 0.00712 **
## Residuals   165  45.95  0.2785                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(span.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = omnibus_span_no_DFR_MRI ~ level, data = split_constructs[["all"]])
## 
## $level
##                diff        lwr         upr     p adj
## med-high -0.1305318 -0.3664026  0.10533899 0.3922824
## low-high -0.3167678 -0.5526386 -0.08089705 0.0050411
## low-med  -0.1862360 -0.4221068  0.04963473 0.1514683
print("LTM")
## [1] "LTM"
LTM.aov <- aov(LTM ~ level, data=split_constructs[["all"]])
summary(LTM.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2   3.12  1.5605   2.822 0.0624 .
## Residuals   161  89.02  0.5529                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingness
TukeyHSD(LTM.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = LTM ~ level, data = split_constructs[["all"]])
## 
## $level
##                diff        lwr          upr     p adj
## med-high -0.1549342 -0.4903700  0.180501649 0.5200105
## low-high -0.3381273 -0.6751126 -0.001142138 0.0490211
## low-med  -0.1831932 -0.5201784  0.153792056 0.4051248
print("Intelligence")
## [1] "Intelligence"
intelligence.aov <- aov(intelligence ~ level, data=split_constructs[["all"]])
summary(intelligence.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.17  0.5867   0.999   0.37
## Residuals   165  96.87  0.5871
TukeyHSD(intelligence.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = intelligence ~ level, data = split_constructs[["all"]])
## 
## $level
##                 diff        lwr       upr     p adj
## med-high -0.02305072 -0.3655146 0.3194132 0.9861279
## low-high -0.18768753 -0.5301514 0.1547763 0.3993499
## low-med  -0.16463682 -0.5071007 0.1778271 0.4927506

Clinical

There is a significant negative correlation between BPRS and performance - this doesn’t come through quite as well in the split groups, but we definitely still see the pattern and there is a trend in differences in the ANOVA.

ggplot(data=data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = WHO_ST_S32))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=55,label="r=-0.13")+
  ggtitle("Correlation of WHODAS and DFR Performance")

print("WHODAS")
## [1] "WHODAS"
print(cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$WHO_ST_S32))
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$WHO_ST_S32
## t = -1.6545, df = 168, p-value = 0.0999
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.27194790  0.02436222
## sample estimates:
##        cor 
## -0.1266163
ggplot(data=data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = BPRS_TOT))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=.9,y=70,label="r=-0.19*")+
  ggtitle("Correlation of BPRS and DFR Performance")

print("BPRS")
## [1] "BPRS"
print(cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$BPRS_TOT))
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$BPRS_TOT
## t = -2.5715, df = 168, p-value = 0.01099
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.33529800 -0.04542107
## sample estimates:
##        cor 
## -0.1946049
clinical_plots[["WHO_ST_S32"]]$labels$title <- "WHODAS"
clinical_plots[["BPRS"]]$labels$title <- "BPRS"

(clinical_plots[["WHO_ST_S32"]] + clinical_plots[["BPRS_TOT"]])+
  plot_annotation(title="Clinical measures split by DFR performance")

print("WHODAS")
## [1] "WHODAS"
WHODAS.aov <- aov(WHO_ST_S32 ~ level, data=split_clinical[["all"]])
summary(WHODAS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   3.61  1.8040   1.857  0.159
## Residuals   165 160.32  0.9717
print("BPRS")
## [1] "BPRS"
BPRS.aov <- aov(BPRS_TOT ~ level, data=split_clinical[["all"]])
summary(BPRS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2   5.45  2.7239   2.844 0.0611 .
## Residuals   165 158.04  0.9578                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Cue Period

Full mask

There is a linear relationship between performance and load effect in the cue mask during the delay period.

fullMask_delay_plots[["cue_low"]]+fullMask_delay_plots[["cue_high"]]+fullMask_delay_plots[["cue_loadEffect"]]+
  plot_annotation(title="BOLD signal from full delay period mask during cue period")

print("Load Effect")
## [1] "Load Effect"
cue_LE.aov <- aov(cue_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(cue_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.944  0.4722   3.082 0.0485 *
## Residuals   165 25.284  0.1532                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(cue_LE.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = cue_loadEffect ~ level, data = split_fullMask_delay[["all"]])
## 
## $level
##                 diff        lwr          upr     p adj
## med-high -0.10768817 -0.2826502  0.067273864 0.3150380
## low-high -0.18268512 -0.3576472 -0.007723088 0.0384648
## low-med  -0.07499695 -0.2499590  0.099965080 0.5692292

Scatter Plots

Let’s look at some scatter plots for the more linear looking things - particularly the high load and the load effect.

data_for_plot <- merge(p200_data,p200_DFR_full_mask,by="PTID")

ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=cue_high))+
  geom_point()+
  stat_smooth(method="lm")+  
  geom_text(x=0.9,y=2,label="r=0.25**")+
  ggtitle("High Load")

print("High Load")
## [1] "High Load"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$cue_high)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$cue_high
## t = 3.32, df = 168, p-value = 0.001104
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1014029 0.3842921
## sample estimates:
##       cor 
## 0.2481301
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=cue_loadEffect))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1.5,label="r=0.24**")+
  
  ggtitle("Load Effect")

print("Load Effect")
## [1] "Load Effect"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$cue_loadEffect)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$cue_loadEffect
## t = 3.2502, df = 168, p-value = 0.001393
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.09623747 0.37983718
## sample estimates:
##       cor 
## 0.2432285

Individual ROIs

The only significant thing here is the occipital regions - more decrease in

DFR_cue_plots[["L_FEF_low"]] +  DFR_cue_plots[["L_FEF_high"]] + DFR_cue_plots[["L_FEF_loadEffect"]]

DFR_cue_plots[["L_insula_low"]] +  DFR_cue_plots[["L_insula_high"]] + DFR_cue_plots[["L_insula_loadEffect"]]

DFR_cue_plots[["L_IPS_low"]] +  DFR_cue_plots[["L_IPS_high"]] + DFR_cue_plots[["L_IPS_loadEffect"]]

DFR_cue_plots[["L_occipital_low"]] +  DFR_cue_plots[["L_occipital_high"]] + DFR_cue_plots[["L_occipital_loadEffect"]]

DFR_cue_plots[["R_FEF_low"]] +  DFR_cue_plots[["R_FEF_high"]] + DFR_cue_plots[["R_FEF_loadEffect"]]

DFR_cue_plots[["R_insula_low"]] +  DFR_cue_plots[["R_insula_high"]] + DFR_cue_plots[["R_insula_loadEffect"]]

DFR_cue_plots[["R_IPS_low"]] +  DFR_cue_plots[["R_IPS_high"]] + DFR_cue_plots[["R_IPS_loadEffect"]]

DFR_cue_plots[["R_MFG_low"]] +  DFR_cue_plots[["R_MFG_high"]] + DFR_cue_plots[["R_MFG_loadEffect"]]

DFR_cue_plots[["R_preSMA_low"]] +  DFR_cue_plots[["R_preSMA_high"]] + DFR_cue_plots[["R_preSMA_loadEffect"]]

DFR_cue_plots[["R_occipital_low"]] +  DFR_cue_plots[["R_occipital_high"]] + DFR_cue_plots[["R_occipital_loadEffect"]]

print("L FEF")
## [1] "L FEF"
cue_L_FEF.aov <- aov(L_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_FEF.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.55  0.2764   1.227  0.296
## Residuals   165  37.17  0.2253
print("L insula")
## [1] "L insula"
cue_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_insula.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.88  0.4398   1.676   0.19
## Residuals   165  43.29  0.2624
print("L IPS")
## [1] "L IPS"
cue_L_IPS.aov <- aov(L_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_IPS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.91  0.9541   2.295  0.104
## Residuals   165  68.60  0.4157
print("L occipital")
## [1] "L occipital"
cue_L_occipital.aov <- aov(L_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_occipital.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2   3.30   1.648    3.59 0.0298 *
## Residuals   165  75.74   0.459                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("R FEF")
## [1] "R FEF"
cue_R_FEF.aov <- aov(R_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_FEF.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   2.08  1.0387   1.907  0.152
## Residuals   165  89.87  0.5447
print("R insula")
## [1] "R insula"
cue_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_insula.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.44  0.2221   1.007  0.367
## Residuals   165  36.39  0.2205
print("R IPS")
## [1] "R IPS"
cue_R_IPS.aov <- aov(R_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_IPS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2   1.85  0.9259   2.374 0.0962 .
## Residuals   165  64.34  0.3899                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("R MFG")
## [1] "R MFG"
cue_R_MFG.aov <- aov(R_MFG_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_MFG.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.597  0.2987   1.594  0.206
## Residuals   165 30.910  0.1873
print("R occipital")
## [1] "R occipital"
cue_R_occipital.aov <- aov(R_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_occipital.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2   3.81  1.9029    4.48 0.0127 *
## Residuals   165  70.09  0.4248                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("R preSMA")
## [1] "R preSMA"
cue_R_preSMA.aov <- aov(R_preSMA_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_preSMA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.71  0.3540   0.759   0.47
## Residuals   165  76.97  0.4665

Delay Period

Full Mask

There are significant differnces in the high load and load effect - the high load trials have differences: high > low and high > medium, while the load effect only has high > low.

fullMask_delay_plots[["delay_low"]]+fullMask_delay_plots[["delay_high"]]+fullMask_delay_plots[["delay_loadEffect"]]+
  plot_annotation(title="BOLD signal from full delay period mask during delay period")

print("Low Load")
## [1] "Low Load"
delay_L1.aov <- aov(delay_low ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L1.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.051 0.02533   0.833  0.437
## Residuals   165  5.018 0.03042
TukeyHSD(delay_L1.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = delay_low ~ level, data = split_fullMask_delay[["all"]])
## 
## $level
##                 diff         lwr        upr     p adj
## med-high -0.01471531 -0.09266343 0.06323281 0.8960257
## low-high  0.02720635 -0.05074177 0.10515447 0.6877751
## low-med   0.04192166 -0.03602646 0.11986978 0.4130273
print("High Load")
## [1] "High Load"
delay_L3.aov <- aov(delay_high ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L3.aov)
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## level         2  0.501 0.25048   7.329 0.000892 ***
## Residuals   165  5.639 0.03417                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(delay_L3.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = delay_high ~ level, data = split_fullMask_delay[["all"]])
## 
## $level
##                  diff         lwr         upr     p adj
## med-high -0.118340890 -0.20096672 -0.03571506 0.0025291
## low-high -0.113164799 -0.19579063 -0.03053897 0.0041193
## low-med   0.005176091 -0.07744974  0.08780192 0.9879721
print("Load Effect")
## [1] "Load Effect"
delay_LE.aov <- aov(delay_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(delay_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)   
## level         2  0.593 0.29673   6.214 0.0025 **
## Residuals   165  7.880 0.04776                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(delay_LE.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = delay_loadEffect ~ level, data = split_fullMask_delay[["all"]])
## 
## $level
##                 diff        lwr          upr     p adj
## med-high -0.10362558 -0.2012982 -0.005952923 0.0347174
## low-high -0.14037115 -0.2380438 -0.042698489 0.0024325
## low-med  -0.03674557 -0.1344182  0.060927091 0.6475245

Scatter Plots

Let’s look at some scatter plots for the more linear looking things - particularly the high load and the load effect.

data_for_plot <- merge(p200_data,p200_DFR_full_mask,by="PTID")

ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=delay_high))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=0.6,label="r=0.26***")+
  
  ggtitle("High Load")

print("High Load")
## [1] "High Load"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$delay_high)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$delay_high
## t = 3.5615, df = 168, p-value = 0.00048
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1191920 0.3995341
## sample estimates:
##      cor 
## 0.264953
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=delay_loadEffect))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1,label="r=0.26***")+
  ggtitle("Load Effect")

print("Load Effect")
## [1] "Load Effect"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$delay_loadEffect)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$delay_loadEffect
## t = 3.4403, df = 168, p-value = 0.0007331
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1102834 0.3919204
## sample estimates:
##       cor 
## 0.2565393

Individual ROIs

No L dMFG, all show high > low, except for . L aMFG, L dlPFC, R dlPFC also showed high > med, and R medial parietal only showed high > med.

(DFR_delay_plots[["DFR_L_aMFG"]] + DFR_delay_plots[["DFR_L_dlPFC"]] + DFR_delay_plots[["DFR_L_dMFG"]]) + plot_annotation(title="individual DFR delay period ROIs")

(DFR_delay_plots[["DFR_L_IPS"]] + DFR_delay_plots[["DFR_L_preSMA"]] + DFR_delay_plots[["DFR_R_dlPFC"]])  

(DFR_delay_plots[["DFR_R_dMFG"]] + DFR_delay_plots[["DFR_R_IPS"]] + DFR_delay_plots[["DFR_R_medParietal"]]) 

print("L aMFG")
## [1] "L aMFG"
L_aMFG.aov <- aov(DFR_L_aMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_aMFG.aov)
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## level         2  1.431  0.7155   9.074 0.000182 ***
## Residuals   165 13.011  0.0789                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_aMFG.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_L_aMFG ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff        lwr         upr     p adj
## med-high -0.14467585 -0.2701834 -0.01916829 0.0193402
## low-high -0.22277889 -0.3482864 -0.09727133 0.0001295
## low-med  -0.07810304 -0.2036106  0.04740451 0.3071420
print("L dlPFC")
## [1] "L dlPFC"
L_dlPFC.aov <- aov(DFR_L_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(L_dlPFC.aov)
##              Df Sum Sq Mean Sq F value  Pr(>F)   
## level         2  0.976  0.4878   7.024 0.00118 **
## Residuals   165 11.459  0.0694                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_dlPFC.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_L_dlPFC ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff        lwr          upr     p adj
## med-high -0.12027344 -0.2380607 -0.002486185 0.0441621
## low-high -0.18376405 -0.3015513 -0.065976794 0.0008848
## low-med  -0.06349061 -0.1812779  0.054296649 0.4113955
print("L dMFG")
## [1] "L dMFG"
L_dMFG.aov <- aov(DFR_L_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_dMFG.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.315 0.15733   2.883 0.0588 .
## Residuals   165  9.005 0.05458                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("L IPS")
## [1] "L IPS"
L_IPS.aov <- aov(DFR_L_IPS ~ level, data=split_DFR_delay[["all"]])
summary(L_IPS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)   
## level         2  0.572 0.28619   5.685 0.0041 **
## Residuals   165  8.306 0.05034                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_IPS.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_L_IPS ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff        lwr         upr     p adj
## med-high -0.06108331 -0.1613662  0.03919954 0.3225542
## low-high -0.14249284 -0.2427757 -0.04220999 0.0027663
## low-med  -0.08140953 -0.1816924  0.01887332 0.1362678
print("L preSMA")
## [1] "L preSMA"
L_preSMA.aov <- aov(DFR_L_preSMA ~ level, data=split_DFR_delay[["all"]])
summary(L_preSMA.aov)
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## level         2  0.867  0.4334    7.62 0.000684 ***
## Residuals   165  9.385  0.0569                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_preSMA.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_L_preSMA ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff        lwr         upr     p adj
## med-high -0.04077966 -0.1473775  0.06581818 0.6380645
## low-high -0.16862032 -0.2752182 -0.06202248 0.0007351
## low-med  -0.12784066 -0.2344385 -0.02124282 0.0141448
print("R dlPFC")
## [1] "R dlPFC"
R_dlPFC.aov <- aov(DFR_R_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(R_dlPFC.aov)
##              Df Sum Sq Mean Sq F value  Pr(>F)   
## level         2  0.853  0.4264   5.675 0.00414 **
## Residuals   165 12.398  0.0751                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_dlPFC.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_R_dlPFC ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff        lwr         upr     p adj
## med-high -0.15604359 -0.2785624 -0.03352479 0.0083964
## low-high -0.14570503 -0.2682238 -0.02318623 0.0151450
## low-med   0.01033856 -0.1121802  0.13285736 0.9782852
print("R dMFG")
## [1] "R dMFG"
R_dMFG.aov <- aov(DFR_R_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(R_dMFG.aov)
##              Df Sum Sq Mean Sq F value  Pr(>F)   
## level         2   0.70   0.350   5.223 0.00632 **
## Residuals   165  11.06   0.067                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_dMFG.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_R_dMFG ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff        lwr          upr     p adj
## med-high -0.12285140 -0.2385489 -0.007153935 0.0345290
## low-high -0.14763033 -0.2633278 -0.031932865 0.0082542
## low-med  -0.02477893 -0.1404764  0.090918540 0.8682684
print("R IPS")
## [1] "R IPS"
R_IPS.aov <- aov(DFR_R_IPS ~ level, data=split_DFR_delay[["all"]])
summary(R_IPS.aov)
##              Df Sum Sq Mean Sq F value  Pr(>F)   
## level         2  0.714  0.3569   4.893 0.00863 **
## Residuals   165 12.035  0.0729                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_IPS.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_R_IPS ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff        lwr         upr     p adj
## med-high -0.09424707 -0.2149556  0.02646142 0.1578139
## low-high -0.15872652 -0.2794350 -0.03801803 0.0062119
## low-med  -0.06447945 -0.1851879  0.05622904 0.4179414
print("R medial Parietal")
## [1] "R medial Parietal"
R_medParietal.aov <- aov(DFR_R_medParietal ~ level, data=split_DFR_delay[["all"]])
summary(R_medParietal.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2   1.56  0.7820   3.956  0.021 *
## Residuals   165  32.61  0.1977                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_medParietal.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_R_medParietal ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff        lwr         upr     p adj
## med-high -0.22691284 -0.4256253 -0.02820033 0.0207656
## low-high -0.17071269 -0.3694252  0.02799981 0.1078362
## low-med   0.05620015 -0.1425124  0.25491265 0.7818678

Scatter Plots

Let’s look at some scatter plots for the more linear looking things - particularly the high load and the load effect.

data_for_plot <- merge(p200_data,p200_indiv_ROI_DFR_delay,by="PTID")

ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_aMFG))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1,label="r=0.27***")+
  ggtitle("L aMFG")

print("L aMFG")
## [1] "L aMFG"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_aMFG)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_aMFG
## t = 3.6757, df = 168, p-value = 0.0003188
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1275544 0.4066460
## sample estimates:
##       cor 
## 0.2728306
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_dlPFC))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1,label="r=0.26***")+
  
  ggtitle("L dlPFC")

print("L dlPFC")
## [1] "L dlPFC"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_dlPFC)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_dlPFC
## t = 3.5387, df = 168, p-value = 0.0005202
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1175198 0.3981079
## sample estimates:
##       cor 
## 0.2633753
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_dMFG))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=0.75,label="r=0.16*")+
  ggtitle("L dMFG")

print("L dMFG")
## [1] "L dMFG"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_dMFG)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_dMFG
## t = 2.0918, df = 168, p-value = 0.03796
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.009023415 0.302579639
## sample estimates:
##       cor 
## 0.1593213
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_IPS))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=0.75,label="r=0.24**")+
  ggtitle("L IPS")

print("L IPS")
## [1] "L IPS"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_IPS)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_IPS
## t = 3.2651, df = 168, p-value = 0.001326
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.09734466 0.38079319
## sample estimates:
##       cor 
## 0.2442798
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_preSMA))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1,label="r=0.28***")+
  ggtitle("L preSMA")

print("L preSMA")
## [1] "L preSMA"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_preSMA)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_preSMA
## t = 3.7087, df = 168, p-value = 0.0002828
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1299569 0.4086830
## sample estimates:
##       cor 
## 0.2750902
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_dlPFC))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1.25,label="r=0.23**")+
  ggtitle("R dlPFC")

print("R dlPFC")
## [1] "R dlPFC"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_dlPFC)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_dlPFC
## t = 3.1068, df = 168, p-value = 0.002221
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.08557902 0.37060306
## sample estimates:
##       cor 
## 0.2330909
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_dMFG))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1.25,label="r=0.20**")+
  ggtitle("R dMFG")

print("R dMFG")
## [1] "R dMFG"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_dMFG)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_dMFG
## t = 2.7004, df = 168, p-value = 0.007634
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.05514113 0.34391879
## sample estimates:
##       cor 
## 0.2039626
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_IPS))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1.25,label="r=0.25**")+
  
  ggtitle("R IPS")

print("R IPS")
## [1] "R IPS"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_IPS)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_IPS
## t = 3.3318, df = 168, p-value = 0.001061
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1022765 0.3850442
## sample estimates:
##       cor 
## 0.2489582
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_medParietal))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1.75,label="r=0.18*")+
  ggtitle("R medial Parietal")

print("R medial Parietal")
## [1] "R medial Parietal"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_medParietal)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_medParietal
## t = 2.4465, df = 168, p-value = 0.01546
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.03596677 0.32686610
## sample estimates:
##       cor 
## 0.1854769

Probe Period

Full Mask

No differences in the probe period.

fullMask_delay_plots[["probe_low"]]+fullMask_delay_plots[["probe_high"]]+fullMask_delay_plots[["probe_loadEffect"]]+
  plot_annotation(title="BOLD signal from full delay period mask during probe period")

print("Low Load")
## [1] "Low Load"
probe_L1.aov <- aov(probe_low ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L1.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.64  0.8220   1.793   0.17
## Residuals   165  75.64  0.4584
print("High Load")
## [1] "High Load"
probe_L3.aov <- aov(probe_high ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   2.79  1.3959   2.013  0.137
## Residuals   165 114.40  0.6933
print("Load Effect")
## [1] "Load Effect"
probe_LE.aov <- aov(probe_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(probe_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   2.67  1.3365    2.09  0.127
## Residuals   165 105.51  0.6395
data_for_plot <- merge(p200_data,p200_DFR_full_mask,by="PTID")

ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=probe_loadEffect))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=2.5,label="r=0.19*")+
  ggtitle("Load Effect")

print("Load Effect")
## [1] "Load Effect"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$probe_loadEffect)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$probe_loadEffect
## t = 2.5975, df = 168, p-value = 0.01022
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.0473804 0.3370397
## sample estimates:
##       cor 
## 0.1964934

Individual ROIs

DFR_probe_plots[["dmPFC_loadEffect"]] + DFR_probe_plots[["L_aMFG_loadEffect"]] + DFR_probe_plots[["L_dlPFC_loadEffect"]] +
  plot_annotation(title="individual DFR activity from probe period regions")

DFR_probe_plots[["L_insula_loadEffect"]] + DFR_probe_plots[["L_IPS_loadEffect"]] + DFR_probe_plots[["R_dlPFC_loadEffect"]] 

DFR_probe_plots[["R_insula_loadEffect"]] + DFR_probe_plots[["R_OFC_loadEffect"]] 

print("dmPFC")
## [1] "dmPFC"
probe_dmPFC.aov <- aov(dmPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_dmPFC.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.12  0.5588   1.894  0.154
## Residuals   165  48.68  0.2950
print("L aMFG")
## [1] "L aMFG"
probe_L_aMFG.aov <- aov(L_aMFG_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_aMFG.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.01  0.0032   0.005  0.995
## Residuals   165 115.38  0.6992
print("L dlPFC")
## [1] "L dlPFC"
probe_L_dlPFC.aov <- aov(L_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_dlPFC.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.97  0.9826   2.253  0.108
## Residuals   165  71.98  0.4362
print("L insula")
## [1] "L insula"
probe_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_insula.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.684  0.3421   1.891  0.154
## Residuals   165 29.844  0.1809
print("R dlPFC")
## [1] "R dlPFC"
probe_R_dlPFC.aov <- aov(R_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_dlPFC.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.17  0.0832   0.205  0.815
## Residuals   165  67.11  0.4068
print("R Insula")
## [1] "R Insula"
probe_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_insula.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.339  0.1693   0.972  0.381
## Residuals   165 28.748  0.1742
print("R OFC")
## [1] "R OFC"
probe_R_OFC.aov <- aov(R_OFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_OFC.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.24  0.1182   0.612  0.544
## Residuals   165  31.88  0.1932

FFA

Only see difference in L FFA load effect during cue, with high > low.

DFR_FFA_plots[["L_CUE_LE"]] + DFR_FFA_plots[["L_DELAY_LE"]] + DFR_FFA_plots[["L_PROBE_LE"]]+
  plot_annotation(title="FFA during DFR task")

DFR_FFA_plots[["R_CUE_LE"]] + DFR_FFA_plots[["R_DELAY_LE"]] + DFR_FFA_plots[["R_PROBE_LE"]]

print("L Cue")
## [1] "L Cue"
L_CUE_LE_FFA.aov <- aov(L_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_CUE_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2   2.69  1.3460   4.023 0.0197 *
## Residuals   164  54.87  0.3346                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_CUE_LE_FFA.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = L_CUE_LE ~ level, data = split_DFR_FFA[["all"]])
## 
## $level
##                diff        lwr         upr     p adj
## med-high -0.1127546 -0.3724683  0.14695908 0.5610187
## low-high -0.3065647 -0.5651059 -0.04802350 0.0155111
## low-med  -0.1938101 -0.4535238  0.06590365 0.1846592
print("R Cue")
## [1] "R Cue"
R_CUE_LE_FFA.aov <- aov(R_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_CUE_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2   1.76  0.8790   2.837 0.0615 .
## Residuals   164  50.81  0.3098                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_FFA.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_DELAY_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.021 0.01036    0.24  0.787
## Residuals   164  7.089 0.04323
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_FFA.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_DELAY_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.069 0.03459   0.915  0.402
## Residuals   164  6.196 0.03778
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_FFA.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_PROBE_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   3.59  1.7951   1.838  0.162
## Residuals   164 160.14  0.9765
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_FFA.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_PROBE_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.89  0.4456   0.726  0.485
## Residuals   164 100.67  0.6138
data_for_plot <- merge(p200_data,p200_FFA,by="PTID")

ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=R_CUE_LE))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=2.25,label="r=0.26***")+
  ggtitle("R Cue LE")

print("R cue Load Effect")
## [1] "R cue Load Effect"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$R_CUE_LE)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$R_CUE_LE
## t = 3.5205, df = 167, p-value = 0.0005553
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1165105 0.3980145
## sample estimates:
##       cor 
## 0.2628475
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=L_CUE_LE))+
  geom_point()+
  stat_smooth(method="lm")+  
  geom_text(x=0.9,y=2.25,label="r=0.29***")+
  ggtitle("L Cue LE")

print("L cue Load Effect")
## [1] "L cue Load Effect"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$L_CUE_LE)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$L_CUE_LE
## t = 4.0284, df = 167, p-value = 8.512e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1535378 0.4292746
## sample estimates:
##    cor 
## 0.2976

HPC

No significant differences in any region of the hippocampus.

Anterior

L3

DFR_HPC_Ant_plots[["L_CUE_L3"]] + DFR_HPC_Ant_plots[["L_DELAY_L3"]] + DFR_HPC_Ant_plots[["L_PROBE_L3"]]+
  plot_annotation(title="HPC Ant during DFR task")

DFR_HPC_Ant_plots[["R_CUE_L3"]] + DFR_HPC_Ant_plots[["R_DELAY_L3"]] + DFR_HPC_Ant_plots[["R_PROBE_L3"]]

print("L Cue")
## [1] "L Cue"
L_CUE_L3_HPC_Ant.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.24  0.1198   0.521  0.595
## Residuals   164  37.69  0.2298
print("R Cue")
## [1] "R Cue"
R_CUE_L3_HPC_Ant.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.34  0.1718   0.778  0.461
## Residuals   164  36.21  0.2208
print("L Delay")
## [1] "L Delay"
L_DELAY_L3_HPC_Ant.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.035 0.01748   0.578  0.562
## Residuals   164  4.957 0.03023
print("R Delay")
## [1] "R Delay"
R_DELAY_L3_HPC_Ant.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.021 0.01032   0.362  0.697
## Residuals   164  4.679 0.02853
print("L Probe")
## [1] "L Probe"
L_PROBE_L3_HPC_Ant.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.31  0.1545   0.302   0.74
## Residuals   164  83.97  0.5120
print("R Probe")
## [1] "R Probe"
R_PROBE_L3_HPC_Ant.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.87  0.4354   0.763  0.468
## Residuals   164  93.54  0.5704

LE

DFR_HPC_Ant_plots[["L_CUE_LE"]] + DFR_HPC_Ant_plots[["L_DELAY_LE"]] + DFR_HPC_Ant_plots[["L_PROBE_LE"]]+
  plot_annotation(title="HPC Ant during DFR task")

DFR_HPC_Ant_plots[["R_CUE_LE"]] + DFR_HPC_Ant_plots[["R_DELAY_LE"]] + DFR_HPC_Ant_plots[["R_PROBE_LE"]]

print("L Cue")
## [1] "L Cue"
L_CUE_LE_HPC_Ant.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.22  0.1107   0.564   0.57
## Residuals   164  32.22  0.1965
print("R Cue")
## [1] "R Cue"
R_CUE_LE_HPC_Ant.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.25  0.1241   0.634  0.532
## Residuals   164  32.11  0.1958
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_HPC_Ant.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.013 0.00674   0.196  0.822
## Residuals   164  5.635 0.03436
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_HPC_Ant.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.029 0.01427    0.43  0.651
## Residuals   164  5.445 0.03320
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_HPC_Ant.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.90  0.4505   0.756  0.471
## Residuals   164  97.78  0.5962
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_HPC_Ant.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.28  0.1418   0.234  0.792
## Residuals   164  99.43  0.6063

Medial

L3

DFR_HPC_Med_plots[["L_CUE_L3"]] + DFR_HPC_Med_plots[["L_DELAY_L3"]] + DFR_HPC_Med_plots[["L_PROBE_L3"]]+
  plot_annotation(title="HPC_Med during DFR task")

DFR_HPC_Med_plots[["R_CUE_L3"]] + DFR_HPC_Med_plots[["R_DELAY_L3"]] + DFR_HPC_Med_plots[["R_PROBE_L3"]]

print("L Cue")
## [1] "L Cue"
L_CUE_L3_HPC_Med.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_L3_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.11 0.05493   0.324  0.724
## Residuals   164  27.81 0.16959
print("R Cue")
## [1] "R Cue"
R_CUE_L3_HPC_Med.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_L3_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.283  0.1417   0.846  0.431
## Residuals   164 27.480  0.1676
print("L Delay")
## [1] "L Delay"
L_DELAY_L3_HPC_Med.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_L3_HPC_Med.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2  0.019 0.009596   0.476  0.622
## Residuals   164  3.307 0.020163
print("R Delay")
## [1] "R Delay"
R_DELAY_L3_HPC_Med.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_L3_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.045 0.02243   1.154  0.318
## Residuals   164  3.188 0.01944
print("L Probe")
## [1] "L Probe"
L_PROBE_L3_HPC_Med.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_L3_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.86  0.4318   1.118  0.329
## Residuals   164  63.33  0.3862
print("R Probe")
## [1] "R Probe"
R_PROBE_L3_HPC_Med.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_L3_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.43  0.7165   1.871  0.157
## Residuals   164  62.80  0.3829

LE

DFR_HPC_Med_plots[["L_CUE_LE"]] + DFR_HPC_Med_plots[["L_DELAY_LE"]] + DFR_HPC_Med_plots[["L_PROBE_LE"]]+
  plot_annotation(title="HPC_Med during DFR task")

DFR_HPC_Med_plots[["R_CUE_LE"]] + DFR_HPC_Med_plots[["R_DELAY_LE"]] + DFR_HPC_Med_plots[["R_PROBE_LE"]]

print("L Cue")
## [1] "L Cue"
L_CUE_LE_HPC_Med.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_LE_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.608  0.3041   2.654 0.0734 .
## Residuals   164 18.789  0.1146                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("R Cue")
## [1] "R Cue"
R_CUE_LE_HPC_Med.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_LE_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.372  0.1862   1.467  0.234
## Residuals   164 20.817  0.1269
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_HPC_Med.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_LE_HPC_Med.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2  0.003 0.001608   0.067  0.935
## Residuals   164  3.934 0.023988
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_HPC_Med.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_LE_HPC_Med.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2  0.019 0.009294   0.421  0.657
## Residuals   164  3.624 0.022097
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_HPC_Med.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_LE_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.22  0.1103   0.258  0.773
## Residuals   164  70.08  0.4273
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_HPC_Med.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_LE_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.80  0.3977   1.094  0.337
## Residuals   164  59.63  0.3636

Posterior

L3

DFR_HPC_Post_plots[["L_CUE_L3"]] + DFR_HPC_Post_plots[["L_DELAY_L3"]] + DFR_HPC_Post_plots[["L_PROBE_L3"]]+
  plot_annotation(title="HPC_Post during DFR task")

DFR_HPC_Post_plots[["R_CUE_L3"]] + DFR_HPC_Post_plots[["R_DELAY_L3"]] + DFR_HPC_Post_plots[["R_PROBE_L3"]]

print("L Cue")
## [1] "L Cue"
L_CUE_L3_HPC_Post.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.333  0.1666   1.177  0.311
## Residuals   164 23.222  0.1416
print("R Cue")
## [1] "R Cue"
R_CUE_L3_HPC_Post.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.461  0.2303   1.668  0.192
## Residuals   164 22.645  0.1381
print("L Delay")
## [1] "L Delay"
L_DELAY_L3_HPC_Post.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0423 0.02117   1.582  0.209
## Residuals   164 2.1944 0.01338
print("R Delay")
## [1] "R Delay"
R_DELAY_L3_HPC_Post.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2 0.0567 0.02833   2.439 0.0904 .
## Residuals   164 1.9053 0.01162                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("L Probe")
## [1] "L Probe"
L_PROBE_L3_HPC_Post.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.38  0.1876   0.635  0.531
## Residuals   164  48.47  0.2955
print("R Probe")
## [1] "R Probe"
R_PROBE_L3_HPC_Post.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.22  0.1085   0.361  0.698
## Residuals   164  49.32  0.3007

LE

DFR_HPC_Post_plots[["L_CUE_LE"]] + DFR_HPC_Post_plots[["L_DELAY_LE"]] + DFR_HPC_Post_plots[["L_PROBE_LE"]]+
  plot_annotation(title="HPC_Post during DFR task")

DFR_HPC_Post_plots[["R_CUE_LE"]] + DFR_HPC_Post_plots[["R_DELAY_LE"]] + DFR_HPC_Post_plots[["R_PROBE_LE"]]

print("L Cue")
## [1] "L Cue"
L_CUE_LE_HPC_Post.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.174 0.08689   0.891  0.412
## Residuals   164 15.999 0.09755
print("R Cue")
## [1] "R Cue"
R_CUE_LE_HPC_Post.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.162 0.08123   0.932  0.396
## Residuals   164 14.293 0.08715
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_HPC_Post.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_LE_HPC_Post.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0065 0.003273    0.18  0.835
## Residuals   164 2.9776 0.018156
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_HPC_Post.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0014 0.00068    0.05  0.951
## Residuals   164 2.2391 0.01365
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_HPC_Post.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.02  0.0084   0.025  0.975
## Residuals   164  54.61  0.3330
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_HPC_Post.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.38  0.1906   0.643  0.527
## Residuals   164  48.63  0.2965

Cortical Thickness

Only see differences in the L probe regions, with med > high.

cortical_thickness_plots[["Cue_RH"]] + cortical_thickness_plots[["Delay_RH"]] + cortical_thickness_plots[["Probe_RH"]] + 
  plot_annotation(title="Cortical Thickness from DFR Full Mask") 

cortical_thickness_plots[["Cue_LH"]] + cortical_thickness_plots[["Delay_LH"]] + cortical_thickness_plots[["Probe_LH"]]

print("L Cue")
## [1] "L Cue"
L_CUE_DFR_thick.aov <- aov(Cue_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_CUE_DFR_thick.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0131 0.006569   0.739  0.479
## Residuals   164 1.4574 0.008887
print("R Cue")
## [1] "R Cue"
R_CUE_DFR_thick.aov <- aov(Cue_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_CUE_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.162 0.08123   0.932  0.396
## Residuals   164 14.293 0.08715
print("L Delay")
## [1] "L Delay"
L_DELAY_DFR_thick.aov <- aov(Delay_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_DELAY_DFR_thick.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.075 0.03748   1.687  0.188
## Residuals   163  3.622 0.02222               
## 1 observation deleted due to missingness
print("R Delay")
## [1] "R Delay"
R_DELAY_DFR_thick.aov <- aov(Delay_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_DELAY_DFR_thick.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0221 0.01106   0.645  0.526
## Residuals   164 2.8152 0.01717
print("L Probe")
## [1] "L Probe"
L_PROBE_DFR_thick.aov <- aov(Probe_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_PROBE_DFR_thick.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0506 0.02532    1.38  0.254
## Residuals   164 3.0086 0.01835
print("R Probe")
## [1] "R Probe"
R_PROBE_DFR_thick.aov <- aov(Probe_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_PROBE_DFR_thick.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2 0.1159 0.05793   3.279 0.0401 *
## Residuals   164 2.8974 0.01767                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_PROBE_DFR_thick.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Probe_RH ~ level, data = split_cortical_thickness_DFR[["all"]])
## 
## $level
##                 diff          lwr        upr     p adj
## med-high  0.06434623  0.004665777 0.12402669 0.0312089
## low-high  0.02676429 -0.032646732 0.08617530 0.5368331
## low-med  -0.03758195 -0.097262405 0.02209851 0.2986389

Resting State Functional Connectivity

Within Network

Differences within FPCN (med > high) and VAN (med and low > high).

RS_plots[["FPCN_FPCN"]] + RS_plots[["DMN_DMN"]] + RS_plots[["DAN_DAN"]]+
  plot_annotation(title="Resting State Functional Connectivity - Within Networks")

RS_plots[["VAN_VAN"]] + RS_plots[["CO_CO"]] + RS_plots[["visual_visual"]]

print("FPCN")
## [1] "FPCN"
FPCN.aov <- aov(FPCN_FPCN ~ level, data=split_RS[["all"]])
summary(FPCN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)  
## level         2 0.0516 0.025801   3.545 0.0311 *
## Residuals   164 1.1936 0.007278                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(FPCN.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = FPCN_FPCN ~ level, data = split_RS[["all"]])
## 
## $level
##                 diff          lwr        upr     p adj
## med-high  0.04303510  0.004729856 0.08134034 0.0234141
## low-high  0.02371511 -0.014417200 0.06184741 0.3075572
## low-med  -0.01931999 -0.057625235 0.01898525 0.4590698
print("DMN")
## [1] "DMN"
DMN.aov <- aov(DMN_DMN ~ level, data=split_RS[["all"]])
summary(DMN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)  
## level         2 0.0476 0.023814   2.538 0.0821 .
## Residuals   164 1.5387 0.009382                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("DAN")
## [1] "DAN"
DAN.aov <- aov(DAN_DAN ~ level, data=split_RS[["all"]])
summary(DAN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0099 0.004967   0.634  0.532
## Residuals   164 1.2843 0.007831
print("VAN")
## [1] "VAN"
VAN.aov <- aov(VAN_VAN ~ level, data=split_RS[["all"]])
summary(VAN.aov)
##              Df Sum Sq Mean Sq F value  Pr(>F)   
## level         2 0.1191 0.05955   7.196 0.00101 **
## Residuals   164 1.3572 0.00828                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(VAN.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = VAN_VAN ~ level, data = split_RS[["all"]])
## 
## $level
##                diff          lwr        upr     p adj
## med-high 0.04593331  0.005087387 0.08677924 0.0232524
## low-high 0.06309417  0.022432651 0.10375569 0.0009513
## low-med  0.01716086 -0.023685067 0.05800678 0.5818832
print("CO")
## [1] "CO"
CO.aov <- aov(CO_CO ~ level, data=split_RS[["all"]])
summary(CO.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0233 0.011627   1.444  0.239
## Residuals   164 1.3205 0.008052
print("CO")
## [1] "CO"
visual.aov <- aov(visual_visual ~ level, data=split_RS[["all"]])
summary(visual.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0086 0.004293   0.249   0.78
## Residuals   164 2.8272 0.017239

Across Network

No across RS network differences.

RS_plots[["FPCN_DMN"]] + RS_plots[["FPCN_DAN"]] + RS_plots[["FCPN_VAN"]]+
  plot_annotation(title="Resting State Functional Connectivity - Across Networks")

RS_plots[["FPCN_CO"]] + RS_plots[["FPCN_visual"]]

print("FPCN DMN")
## [1] "FPCN DMN"
FPCN_DMN.aov <- aov(FPCN_DMN ~ level, data=split_RS[["all"]])
summary(FPCN_DMN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0194 0.009688   1.196  0.305
## Residuals   164 1.3289 0.008103
print("FPCN DAN")
## [1] "FPCN DAN"
FPCN_DAN.aov <- aov(FPCN_DAN ~ level, data=split_RS[["all"]])
summary(FPCN_DAN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0129 0.006428   1.254  0.288
## Residuals   164 0.8405 0.005125
print("FPCN VAN")
## [1] "FPCN VAN"
FPCN_VAN.aov <- aov(FPCN_VAN ~ level, data=split_RS[["all"]])
summary(FPCN_VAN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0064 0.003187   0.459  0.633
## Residuals   164 1.1396 0.006949
print("FPCN CO")
## [1] "FPCN CO"
FPCN_CO.aov <- aov(FPCN_CO ~ level, data=split_RS[["all"]])
summary(FPCN_CO.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0115 0.005748   0.556  0.574
## Residuals   164 1.6946 0.010333
print("FPCN visual")
## [1] "FPCN visual"
FPCN_visual.aov <- aov(FPCN_visual ~ level, data=split_RS[["all"]])
summary(FPCN_visual.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0004 0.000186   0.025  0.976
## Residuals   164 1.2309 0.007506

Beta Series Connectivity

Cue Period

High Load

Differences in the beta series connectivity for FPCN/HPC connectivity (med > high), FPCN/FFA (med and low > high), and HPC/FFA (med > high)

beta_conn_cue_plots[["FPCN_FPCN_L3"]] + beta_conn_cue_plots[["FPCN_HPC_L3"]] +
  plot_annotation(title = "Beta Series Connectivity at High Load")

beta_conn_cue_plots[["FPCN_FFA_L3"]] + beta_conn_cue_plots[["HPC_FFA_L3"]]

FPCN_FPCN_BC_cue_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.214  0.1070   0.821  0.442
## Residuals   164 21.371  0.1303
FPCN_HPC_BC_cue_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.871  0.4355   4.173 0.0171 *
## Residuals   164 17.115  0.1044                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(FPCN_HPC_BC_cue_L3.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = FPCN_HPC_L3 ~ level, data = split_beta_conn_cue[["all"]])
## 
## $level
##                 diff         lwr        upr     p adj
## med-high  0.17589316  0.03084184 0.32094447 0.0129087
## low-high  0.10547445 -0.03892200 0.24987090 0.1979807
## low-med  -0.07041871 -0.21547002 0.07463261 0.4859175
FPCN_FFA_BC_cue_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_L3.aov)
##              Df Sum Sq Mean Sq F value  Pr(>F)   
## level         2  1.304  0.6521   6.415 0.00208 **
## Residuals   164 16.672  0.1017                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(FPCN_FFA_BC_cue_L3.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = FPCN_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
## 
## $level
##                diff         lwr       upr     p adj
## med-high 0.16024635  0.01708777 0.3034049 0.0240437
## low-high 0.20548974  0.06297748 0.3480020 0.0023437
## low-med  0.04524339 -0.09791520 0.1884020 0.7355717
HPC_FFA_BC_cue_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.691  0.3457   3.558 0.0307 *
## Residuals   164 15.932  0.0971                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(HPC_FFA_BC_cue_L3.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = HPC_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
## 
## $level
##                diff          lwr       upr     p adj
## med-high  0.1423694  0.002421533 0.2823173 0.0451697
## low-high  0.1293965 -0.009919540 0.2687126 0.0747075
## low-med  -0.0129729 -0.152920794 0.1269750 0.9738527

Scatter Plots

data_for_plot <- merge(p200_data,p200_beta_conn_cue,by="PTID")

ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=FPCN_FFA_L3))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=2,label="r=-0.22**")+
  ggtitle("FPCN/FFA L3")

print("FPCN/FFA L3")
## [1] "FPCN/FFA L3"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$FPCN_FFA_L3)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$FPCN_FFA_L3
## t = -2.8628, df = 167, p-value = 0.004738
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.35563295 -0.06752927
## sample estimates:
##        cor 
## -0.2162844

Load Effect

For the load effects, however, we only see FPCN/FPCN (med > high) and FPCN/FFA (low > high).

beta_conn_cue_plots[["FPCN_FPCN_LE"]] + beta_conn_cue_plots[["FPCN_HPC_LE"]] +
  plot_annotation(title = "Beta Series Connectivity Load Effect")

beta_conn_cue_plots[["FPCN_FFA_LE"]] + beta_conn_cue_plots[["HPC_FFA_LE"]]

FPCN_FPCN_BC_cue_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  1.024  0.5122   3.384 0.0363 *
## Residuals   164 24.825  0.1514                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(FPCN_FPCN_BC_cue_LE.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = FPCN_FPCN_LE ~ level, data = split_beta_conn_cue[["all"]])
## 
## $level
##                 diff          lwr       upr     p adj
## med-high  0.17248805 -0.002204571 0.3471807 0.0537927
## low-high  0.15857460 -0.015329335 0.3324785 0.0818754
## low-med  -0.01391345 -0.188606066 0.1607792 0.9806297
FPCN_HPC_BC_cue_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.315  0.1575   1.411  0.247
## Residuals   164 18.313  0.1117
FPCN_FFA_BC_cue_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  1.132  0.5659   3.298 0.0394 *
## Residuals   164 28.141  0.1716                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(FPCN_FFA_BC_cue_LE.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = FPCN_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
## 
## $level
##                diff           lwr       upr     p adj
## med-high 0.15990313 -0.0260913353 0.3458976 0.1074873
## low-high 0.18575564  0.0006008839 0.3709104 0.0490621
## low-med  0.02585251 -0.1601419506 0.2118470 0.9421874
HPC_FFA_BC_cue_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.10 0.04998   0.314  0.731
## Residuals   164  26.15 0.15943

Delay Period

No differences for the beta series connectivity during delay period.

High Load

beta_conn_delay_plots[["FPCN_FPCN_L3"]] + beta_conn_delay_plots[["FPCN_HPC_L3"]] +
  plot_annotation(title = "Beta Series Connectivity at High Load")

beta_conn_delay_plots[["FPCN_FFA_L3"]] + beta_conn_delay_plots[["HPC_FFA_L3"]]

FPCN_FPCN_BC_delay_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.178 0.08922   0.854  0.428
## Residuals   164 17.132 0.10446
FPCN_HPC_BC_delay_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.276 0.13786    1.91  0.151
## Residuals   164 11.837 0.07218
FPCN_FFA_BC_delay_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.098 0.04920   0.837  0.435
## Residuals   164  9.645 0.05881
HPC_FFA_BC_delay_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.29 0.14514   2.294  0.104
## Residuals   164  10.38 0.06327

Scatter plots

data_for_plot <- merge(p200_data,p200_beta_conn_delay,by="PTID")

ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=FPCN_HPC_L3))+
  geom_point()+
  stat_smooth(method="lm")+
  geom_text(x=0.9,y=1.25,label="r=-0.18*")+
  ggtitle("FPCN/HPC L3")

print("FPCN/HPC L3")
## [1] "FPCN/HPC L3"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$FPCN_HPC_L3)
## 
##  Pearson's product-moment correlation
## 
## data:  data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$FPCN_HPC_L3
## t = -2.3698, df = 167, p-value = 0.01894
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.32254842 -0.03023422
## sample estimates:
##       cor 
## -0.180371

Load Effect

beta_conn_delay_plots[["FPCN_FPCN_LE"]] + beta_conn_delay_plots[["FPCN_HPC_LE"]] +
  plot_annotation(title = "Beta Series Connectivity Load Effect")

beta_conn_delay_plots[["FPCN_FFA_LE"]] + beta_conn_delay_plots[["HPC_FFA_LE"]]

FPCN_FPCN_BC_delay_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.018 0.00904   0.192  0.825
## Residuals   164  7.714 0.04704
FPCN_HPC_BC_delay_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.063 0.03153   0.501  0.607
## Residuals   164 10.319 0.06292
FPCN_FFA_BC_delay_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.181 0.09059   1.305  0.274
## Residuals   164 11.384 0.06942
HPC_FFA_BC_delay_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.225  0.1126   1.279  0.281
## Residuals   164 14.433  0.0880
TukeyHSD(HPC_FFA_BC_delay_LE.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
## 
## $level
##                 diff        lwr        upr     p adj
## med-high -0.05287245 -0.1860714 0.08032648 0.6165419
## low-high -0.08915921 -0.2217568 0.04343837 0.2526714
## low-med  -0.03628675 -0.1694857 0.09691218 0.7958256

BCT Measures

No differences in any of the BCT measures.

Overall Measures

BCT_plots[["Participation_Coef_Mean"]] + BCT_plots[["Global_Eff"]] + BCT_plots[["Modularity_Louvain_N"]]+
  plot_annotation(title="Overall BCT Measures")

print("Mean Participation Coefficient")
## [1] "Mean Participation Coefficient"
partic_coef_mean.aov <- aov(Participation_Coef_Mean ~ level, data = split_BCT[["all"]])
summary(partic_coef_mean.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.04 0.01992   0.277  0.759
## Residuals   164  11.81 0.07199
print("Global Efficiency")
## [1] "Global Efficiency"
global_eff.aov <- aov(Global_Eff ~ level, data = split_BCT[["all"]])
summary(global_eff.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0139 0.006959   0.945  0.391
## Residuals   164 1.2074 0.007362
print("Modularity")
## [1] "Modularity"
modularity.aov <- aov(Modularity_Louvain_N ~ level, data = split_BCT[["all"]])
summary(modularity.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2    3.1   1.525   0.211   0.81
## Residuals   164 1186.7   7.236

Individual Networks

indiv_partic_coeff_plots[["FrontoParietal"]] + indiv_partic_coeff_plots[["Default"]] + indiv_partic_coeff_plots[["DorsalAttn"]]+
  plot_annotation(title="Individual Network Participation Coefficient")

indiv_partic_coeff_plots[["CinguloOperc"]] + indiv_partic_coeff_plots[["VentralAttn"]] + indiv_partic_coeff_plots[["Visual"]]

print("FPCN")
## [1] "FPCN"
FPCN_indiv_coeff.aov <- aov(FrontoParietal ~ level, data = split_indiv_partic_coeff[["all"]])
summary(FPCN_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.631  0.3153   1.779  0.172
## Residuals   164 29.067  0.1772
print("DMN")
## [1] "DMN"
DMN_indiv_coeff.aov <- aov(Default ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DMN_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.035 0.01756   0.097  0.908
## Residuals   164 29.821 0.18183
print("DAN")
## [1] "DAN"
DAN_indiv_coeff.aov <- aov(DorsalAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DAN_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.234 0.11718   1.376  0.256
## Residuals   164 13.969 0.08517
print("CO")
## [1] "CO"
CO_indiv_coeff.aov <- aov(CinguloOperc ~ level, data = split_indiv_partic_coeff[["all"]])
summary(CO_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.30  0.1500    1.27  0.284
## Residuals   164  19.37  0.1181
print("VAN")
## [1] "VAN"
VAN_indiv_coeff.aov <- aov(VentralAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(VAN_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.065 0.03235    0.28  0.756
## Residuals   164 18.981 0.11574
print("visual")
## [1] "visual"
visual_indiv_coeff.aov <- aov(Visual ~ level, data = split_indiv_partic_coeff[["all"]])
summary(visual_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.086 0.04307   0.244  0.783
## Residuals   164 28.893 0.17618